Secure multiparty detection of sensitive data using Private Set Intersection (PSI)

ABSTRACT

A method, apparatus and computer program product to detect whether specific sensitive data of a client is present in a cloud computing infrastructure is implemented without requiring that data be shared with the cloud provider, or that the cloud provider provide the client access to all data in the cloud. Instead of requiring the client to share its database of sensitive information, preferably the client executes a tool that uses a cryptographic protocol, namely, Private Set Intersection (PSI), to enable the client to detect whether their sensitive information is present on the cloud. Any such information identified by the tool is then used to label a document or utterance, send an alert, and/or redact or tokenize the sensitive data.

BACKGROUND OF THE INVENTION Technical Field

This disclosure relates generally to identifying and preventingsensitive data leakage in a computing environment.

Background of the Related Art

When using cognitive systems that deal with large amounts of textualdata, users in regulated industries (e.g., hospitals, financialinstitutions, and other regulated clients) have a need to detect whethersensitive data, such as Personally Identifying Information (PII),Protected Health Information (PHI) or the like, has been placed in acomputing system implemented in a third party computing environment,such as a network-accessible cloud computing infrastructure. Thus, forexample, these types of organizations possess definitive lists of theirclients' names and other sensitive information, but they do not want toput those lists into any cloud system. One approach to protectingagainst disclosure of such sensitive information is to use knowntechniques, such as Data Loss Prevention (DLP) systems, which can storecomprehensive databases of personal information and then monitor systemsto detect potential leakage of such information. Although these types ofsystems work well for their intended purposes, they are often complex toimplement and expensive to operate. Further, they do not address therequirement that enterprises desire assurance from their cloud providersthat such information is not on the cloud.

DLP and other known security tools and methods typically usesophisticated techniques to determine whether such sensitive data isleaked or otherwise present on the cloud. In one common approach, astatistical classifier is trained (e.g., using machine learning), withthe resulting model applied to detect for the sensitive data inquestion. But such detection (after-the-fact and typically occurring inthe cloud) cannot approach the accuracy (presumably at or near 100%)that a local rules-based system with access to a full client databasemight provide.

BRIEF SUMMARY

According to this disclosure, a method to detect whether specificsensitive data of a client is present in a cloud computinginfrastructure is implemented without requiring that data be shared withthe cloud provider, or that the cloud provider provide the client accessto the data its stores in the cloud, even on behalf of the client.Instead of requiring the client to share its database of sensitiveinformation, or requiring the cloud provider to expose access to acorpus of information that it stores in the cloud, the client andprovider collaboratively determine whether sensitive data of interest tothe client is found in some portion of the cloud data (typically anindex). This determination is made even while retaining the sensitivedata of interest as private to the client and the index as private tothe cloud provider. To this end, each party executes a tool that uses acryptographic protocol, e.g., a Private Set Intersection (PSI), toenable a party (whether provider or client, or perhaps both) to detectwhether the client's sensitive information is present on the cloud.Information identified by the tool is then used to label a document orutterance, send an alert, and/or redact or tokenize the sensitive data.

According to a more specific aspect, this disclosure describes a methodto protect data that is carried out in association with a cloudcomputing environment, the cloud computing environment comprising a datastore in which a corpus of information is received and stored. Themethod begins by identifying a first set of data associated with thecorpus of information and private to the cloud computing environment.The first set of data may be an index of the corpus of information. Uponreceipt of a request from an entity, a detection technique is thenimplemented. The entity has a second set of data, and wherein the secondset of data is of interest to the entity and private to the entity.Sometimes the second set of data is referred to as the entity's“sensitive data.” An example set of data may be a secret list of wordsor phrases. The first set of data is private to the provider while thesecond set of data is private to the entity; stated another way,typically the entity does not know the first set of data and theprovider does not know the second set of data. Nevertheless, the partiescollaboratively execute a cryptographic protocol to detect presence inthe first set of data of any of the second set of data of interest tothe entity. The cryptographic protocol is executed by evaluating a givencryptographic function over the first and second sets of private datawithout enabling the entity access to the corpus of information or thefirst set of private data. Preferably, the cryptographic protocol is aPrivate Set Intersection (PSI) that the provider executescollaboratively with the entity, e.g., using a Garbled circuitimplemented using an oblivious transfer cryptographic primitive as thegiven cryptographic function. Upon detecting in the first set of dataany of the second set of data of interest to the entity, a given actionis taken. The given action typically is one of: providing an alert,labeling the detected information, applying a token to the detectedinformation, and redacting the detected information.

The foregoing has outlined some of the more pertinent features of thesubject matter. These features should be construed to be merelyillustrative. Many other beneficial results can be attained by applyingthe disclosed subject matter in a different manner or by modifying thesubject matter as will be described.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 depicts an exemplary block diagram of a distributed dataprocessing environment in which exemplary aspects of the illustrativeembodiments may be implemented;

FIG. 2 is an exemplary block diagram of a data processing system inwhich exemplary aspects of the illustrative embodiments may beimplemented;

FIG. 3 illustrates an exemplary cloud computing architecture in whichthe disclosed subject matter may be implemented;

FIG. 4 is a representative cognitive services computing environment inwhich the techniques of this disclosure may be implemented; and

FIG. 5 depicts the basic technique of this disclosure.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

With reference now to the drawings and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments of the disclosure may beimplemented. It should be appreciated that FIGS. 1-2 are only exemplaryand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the disclosedsubject matter may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

Client-Server Technologies

With reference now to the drawings, FIG. 1 depicts a pictorialrepresentation of an exemplary distributed data processing system inwhich aspects of the illustrative embodiments may be implemented.Distributed data processing system 100 may include a network ofcomputers in which aspects of the illustrative embodiments may beimplemented. The distributed data processing system 100 contains atleast one network 102, which is the medium used to provide communicationlinks between various devices and computers connected together withindistributed data processing system 100. The network 102 may includeconnections, such as wire, wireless communication links, or fiber opticcables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example. Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe disclosed subject matter, and therefore, the particular elementsshown in FIG. 1 should not be considered limiting with regard to theenvironments in which the illustrative embodiments of the presentinvention may be implemented.

With reference now to FIG. 2, a block diagram of an exemplary dataprocessing system is shown in which aspects of the illustrativeembodiments may be implemented. Data processing system 200 is an exampleof a computer, such as client 110 in FIG. 1, in which computer usablecode or instructions implementing the processes for illustrativeembodiments of the disclosure may be located.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as server104 or client 110 in FIG. 1, in which computer-usable program code orinstructions implementing the processes may be located for theillustrative embodiments. In this illustrative example, data processingsystem 200 includes communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor (SMP) system containing multiple processors of the sametype.

Memory 206 and persistent storage 208 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory206, in these examples, may be, for example, a random access memory orany other suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 also may be removable. For example, a removablehard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer-usable program code, or computer-readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer-readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer-readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer-readable media 218 form computerprogram product 220 in these examples. In one example, computer-readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer-readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer-readable media 218 is also referred to ascomputer-recordable storage media. In some instances,computer-recordable media 218 may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer-readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. Thecomputer-readable media also may take the form of non-tangible media,such as communications links or wireless transmissions containing theprogram code. The different components illustrated for data processingsystem 200 are not meant to provide architectural limitations to themanner in which different embodiments may be implemented. The differentillustrative embodiments may be implemented in a data processing systemincluding components in addition to or in place of those illustrated fordata processing system 200. Other components shown in FIG. 2 can bevaried from the illustrative examples shown. As one example, a storagedevice in data processing system 200 is any hardware apparatus that maystore data. Memory 206, persistent storage 208, and computer-readablemedia 218 are examples of storage devices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object-oriented programming language such asJava™, Smalltalk, C++, C#, Objective-C, or the like, and conventionalprocedural programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1-2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 1-2. Also, theprocesses of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thedisclosed subject matter.

As will be seen, the techniques described herein may operate inconjunction within the standard client-server paradigm such asillustrated in FIG. 1 in which client machines communicate with anInternet-accessible Web-based portal executing on a set of one or moremachines. End users operate Internet-connectable devices (e.g., desktopcomputers, notebook computers, Internet-enabled mobile devices, or thelike) that are capable of accessing and interacting with the portal.Typically, each client or server machine is a data processing systemsuch as illustrated in FIG. 2 comprising hardware and software, andthese entities communicate with one another over a network, such as theInternet, an intranet, an extranet, a private network, or any othercommunications medium or link. A data processing system typicallyincludes one or more processors, an operating system, one or moreapplications, and one or more utilities. The applications on the dataprocessing system provide native support for Web services including,without limitation, support for HTTP, SOAP, XML, WSDL, UDDI, and WSFL,among others. Information regarding SOAP, WSDL, UDDI and WSFL isavailable from the World Wide Web Consortium (W3C), which is responsiblefor developing and maintaining these standards; further informationregarding HTTP and XML is available from Internet Engineering Task Force(IETF). Familiarity with these standards is presumed.

Cloud Computing Model

An emerging information technology (IT) delivery model is cloudcomputing, by which shared resources, software and information areprovided over the Internet to computers and other devices on-demand.Cloud computing can significantly reduce IT costs and complexities whileimproving workload optimization and service delivery. With thisapproach, an application instance can be hosted and made available fromInternet-based resources that are accessible through a conventional Webbrowser over HTTP. An example application might be one that provides acommon set of messaging functions, such as email, calendaring, contactmanagement, and instant messaging. A user would then access the servicedirectly over the Internet. Using this service, an enterprise wouldplace its email, calendar and/or collaboration infrastructure in thecloud, and an end user would use an appropriate client to access his orher email, or perform a calendar operation.

Cloud compute resources are typically housed in large server farms thatrun one or more network applications, typically using a virtualizedarchitecture wherein applications run inside virtual servers, orso-called “virtual machines” (VMs), that are mapped onto physicalservers in a data center facility. The virtual machines typically run ontop of a hypervisor, which is a control program that allocates physicalresources to the virtual machines.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models, all as more particularly described anddefined in “Draft NIST Working Definition of Cloud Computing” by PeterMell and Tim Grance, dated Oct. 7, 2009.

In particular, the following are typical characteristics:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

The Service Models typically are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

The Deployment Models typically are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service-oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes. A representative cloud computing nodeis as illustrated in FIG. 2 above. In particular, in a cloud computingnode there is a computer system/server, which is operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system/server include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like. Computer system/servermay be described in the general context of computer system-executableinstructions, such as program modules, being executed by a computersystem. Generally, program modules may include routines, programs,objects, components, logic, data structures, and so on that performparticular tasks or implement particular abstract data types. Computersystem/server may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

Referring now to FIG. 3, by way of additional background, a set offunctional abstraction layers provided by a cloud computing environmentis shown. It should be understood in advance that the components,layers, and functions shown in FIG. 3 are intended to be illustrativeonly and embodiments of the invention are not limited thereto. Asdepicted, the following layers and corresponding functions are provided:

Hardware and software layer 300 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide)

Virtualization layer 302 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 304 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provides pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 306 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; enterprise-specific functions in a private cloud; and,according to this disclosure, a secure PSI-data detection technique 308.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the disclosed technique are capable of being implementedin conjunction with any other type of computing environment now known orlater developed. These include standalone computing environments (e.g.,an on-premises desktop machine), client-server-based architectures, andthe like.

Thus, a representative cloud computing environment has a set of highlevel functional components that include a front end identity manager, abusiness support services (BSS) function component, an operationalsupport services (OSS) function component, and the compute cloudcomponent. The identity manager is responsible for interfacing withrequesting clients to provide identity management, and this componentmay be implemented with one or more known systems, such as the TivoliFederated Identity Manager (TFIM) that is available from IBMCorporation, of Armonk, N.Y. In appropriate circumstances TFIM may beused to provide federated single sign-on (F-SSO) to other cloudcomponents. The business support services component provides certainadministrative functions, such as billing support. The operationalsupport services component is used to provide provisioning andmanagement of the other cloud components, such as virtual machine (VM)instances. The cloud component represents the main computationalresources, which are typically a plurality of virtual machine instancesthat are used to execute a target application that is being madeavailable for access via the cloud. One or more databases are used tostore directory, log, and other working data. All of these components(included the front end identity manager) are located “within” thecloud, but this is not a requirement. In an alternative embodiment, theidentity manager may be operated externally to the cloud. The serviceprovider also may be operated externally to the cloud.

Some clouds are based upon non-traditional IP networks. Thus, forexample, a cloud may be based upon two-tier CLOS-based networks withspecial single layer IP routing using hashes of MAC addresses. Thetechniques described herein may be used in such non-traditional clouds.

Generalizing, the cloud computing infrastructure provides for a virtualmachine hosting environment that comprises host machines (e.g., serversor like physical machine computing devices) connected via a network andone or more management servers. Typically, the physical servers are eachadapted to dynamically provide one or more virtual machines usingvirtualization technology, such as VMware ESX/ESXi. Multiple VMs can beplaced into a single host machine and share the host machine's CPU,memory and other resources, thereby increasing the utilization of anorganization's data center. Among other tasks, the management servermonitors the infrastructure and automatically manipulates the VMplacement as needed, e.g., by moving virtual machines between hosts.

In a non-limiting implementation, representative platform technologiesare, without limitation, IBM System x® servers with VMware vSphere 4.1Update 1 and 5.0.

In one embodiment, the technique of this disclosure is implemented inassociation with a cloud computing infrastructure (IBM Cloud) thatsupports cognitive services, such as IBM® Watson™ Assistant (formerlyWatson Conversation). Watson Assistant may be implemented in associationwith an enterprise's cloud application that is supported in a cloudcomputing infrastructure, such as described above with respect to FIG.3. FIG. 4 depicts a representative implementation. In this example,users 400 interact with the cloud application 402 through a userinterface 404, e.g., a simple chat window, a mobile app, a robot with avoice interface, etc. The application 402 sends the user input to theWatson Assistant service 406. The application 402 then connects to aworkspace, 408, which is a container for dialog flow and training data.The service 406 interprets the user input, directs the flow of theconversation, and gathers information that it needs. Additional Watsonservices 410, such as Tone Analyzer or Speech-to-Text, may be connectedas needed, to analyze user input. In addition, the application 402 alsocan interact with enterprise back-end systems 412, e.g., based on theuser's intent and additional information. Using the architecture, theenterprise can program multi-turn dialog and provide response variationsbased on different conditions, collect and validate information, addhandlers for off-topic queries, enable users to browse from a catalog ofalready configured customer service and industry content packs to savetime and start faster, provide analytics and recommendations that revealinsights from conversations and tailor the enterprise's training of theWatson service, protect generated insights, and the like.

In this approach, the enterprise implements a conversation (with Watson)by configuring a workspace using a graphical environment. During thisprocess, the enterprise sets up training data and dialog for theconversation. The training data typically comprises artifacts, namely,intents (goals that users have when they interact with the service) andentities (a term or object that provides context for an intent). Astraining data is added, a natural language classifier is automaticallyadded to the workspace, and it is trained to understand the types ofrequests that the service should listen for and respond to. Using adialog tool, the enterprise can build a dialog flow that incorporatesintents and entities. Typically, the dialog flow is representedgraphically in the tool as a tree. Once configured, the workspace isdeployed by connecting it to a front-end user interface, social media ora messaging channel.

The above-described commercial implementation is not intended to belimited, but rather is simply one representative embodiment of a clientapplication supported in a cloud computing environment and thatinteracts with a cognitive service.

Private Set Intersection

A Private Set Intersection (PSI) protocol enables two parties, each witha private set of data, to securely compute the intersection of theirdata sets. This type of protocol allows mutually untrusted parties tocompute jointly the intersection of their private input sets. Most PSIschemes are single-output, meaning that one of the parties receives theoutput of the intersection while the other does not; other PSI schemesare mutual, wherein the intersection is output to both parties. PSIprotocols have been implemented in various use cases, such as onlinerecommendation services.

Representative PSI protocol implementations may be based on one or morecryptographic protocols. These include, without limitation,Yao-construct Garbled circuits, and Partially Homomorphic Encryption.

Secure Regulated Data Protection Using a Cryptographic PSI Protocol

With the above as background, the subject matter of this disclosure isnow described.

As noted above, this disclosure describes a method and system to protectdata that is carried out in association with a cloud computingenvironment, the cloud computing environment comprising a data store inwhich a corpus of information is received and stored. The method beginsby identifying a first set of data associated with the corpus ofinformation and private to the cloud computing environment. The notionof identifying should be broadly construed as generating, receiving,obtaining or otherwise building the first set of data. The first set ofdata may be generated in advance, or on-the-fly in response to a givenoccurrence, such as a receipt of a request. According to the method, andupon receipt of a request from entity, a detection technique is thenimplemented. The entity has a second set of data, and wherein the secondset of data is of interest to the entity and private to the entity.Sometimes the second set of data is referred to as the entity's“sensitive data.” An example set of data may be a secret list of wordsor phrases. The first set of data is private to the provider while thesecond set of data is private to the entity; stated another way,typically the entity does not know the first set of data and theprovider does not know the second set of data. Nevertheless, the partiescollaboratively execute a cryptographic protocol to detect presence inthe first set of data of any of the second set of data of interest tothe entity. The cryptographic protocol is executed by evaluating a givencryptographic function over the first and second sets of private datawithout enabling the entity access to the corpus of information or thefirst set of private data. Preferably, the cryptographic protocol is aPrivate Set Intersection (PSI) that the provider executescollaboratively with the entity, e.g., using a Garbled circuitimplemented using an oblivious transfer cryptographic primitive as thegiven cryptographic function. Upon detecting in the first set of dataany of the second set of data of interest to the entity, a given actionis taken. The given action typically is one of: providing an alert,labeling the detected information, applying a token to the detectedinformation, and redacting the detected information.

FIG. 5 depicts the basic technique of this disclosure. In this exampleembodiment, cloud computing infrastructure 500 comprises a data store502 that hosts a corpus repository, typically with access controls. Thecloud provider executes a cloud provider Private Set Intersection (PSI)tool 504, in association with a response service 506 that providesresponsive actions, such as one or more of: alerting, redaction,tokenization, labelling, sandboxing, and the like. Preferably, and aswill be described, the data store 502 stores an entire set of content(information), although the tool 504 itself just operates on an index ofthat set of content. The enterprise computing environment 508, whichtypically is hosted in an enterprise private network, comprises adatabase 510 of sensitive data (e.g., PII, PHI, or the like), as well asan instances of both the PSI tool 512 and the response service 514.Enterprise-based resources communicate with cloud provider-basedresources via client-server based communications, such as describedabove in FIG. 1. Each side of the communication link is implemented inone or more data processing systems, such as described and depicted inFIG. 2. The cloud computing infrastructure may be implemented asdescribed in FIG. 3, and it may utilize one or more services such asWatson Assistant, in the manner described in FIG. 4. The client PSItools (504 and 512) interoperate with one another to implement a PSIprotocol exchange, with the cloud-based tool evaluating the index of theset of content stored in the cloud data store The response services 506and 514 typically execute as software (one or more computer systems,programs, processes, etc.) executing in hardware or virtual machines.

As noted above, the Private Set Intersection protocol, which is a formof secure multi-party computation (MPC), enables the two parties (thecloud provider, on the one hand, and the enterprise, on the other hand)to learn if they have a piece of information in common, and withouteither party having to reveal the compared information to the otherparty. With this approach, the index of an arbitrarily large corpus 502in the cloud computing environment 500 is examined, preferably in anautomated manner, and the response service(s) 506 and 514 flag or redactanything that is in the client's full, definitive list of sensitive data510 (e.g., patient names and record numbers, or any other piece ofinformation that the client considers sensitive) without revealing tothe service provider any new information that is not already present onthe cloud. In effect, this approach thus provides for a “zeroknowledge”-based proof regarding whether sensitive data is or is notpresent on the cloud (in other words, in the index), all withoutdisclosing such information to facilitate the evaluation process itself.

In this approach, preferably the sensitive data never leaves the clientpremises 508; rather, the database 510 containing the sensitive dataconnects to the client-side agent 512, which performs Private SetIntersection (PSI) interactively with the cloud-supported PSI agent 504(which, as noted above, preferably examines its index of the informationstored in the cloud, rather than examining that entire set ofinformation itself), thereby detecting, for example, whether sensitivedata fields or any API field that enterprise users populate through aclient application (not shown) from the client-side database 510 arepresent in any document or other object the cloud provider is permittedor allowed to access.

In a preferred embodiment, the cloud provider PSI tool (agent) 504connects to the corpus repository 502 containing an indexed corpus. ThePSI protocol then is performed on the contents of the index. Thisoperation may include only a corpus specific to a particular client, ora broader corpus to which the client has access for sensitiveinformation detection. This embodiment allows clients to determinewhether their sensitive information exists, even in a corpus to whichthey do not have full (or even any) access, a provider-owned or curatedcorpus. As described with respect to FIG. 4, in this embodimentpreferably the cloud provider-based PSI agent 504 integrates directlywith the cognitive service APIs, performing PSI with the client's PSIagent in real-time to detect the passing of sensitive information intext fields as information enters the system. This embodiment thusallows the APIs (e.g., cognitive service APIs) to provide a real-timeindication of apparent entry of sensitive data so that the clientapplication can use the response service 506 (or the like) to warn theclient or the end user and/or redact the data before it is stored on thecloud.

In an alternative embodiment, the PSI interaction is carried out betweenthe cloud provider and a trusted third party (e.g., law enforcement, anintelligence agency, a contracted security organization, companyauditors, authorized partners, etc.), where the trusted third party hasa legitimate interest in detecting the presence of certain sensitiveinformation, e.g., in a cognitive system, typically on behalf of theclient. In this scenario, preferably the third party is not granted fullaccess to the corpus or API, but still has a legitimate interest indetecting, for example, certain sensitive data (e.g., the names ofpersons of interest) in the cognitive system. Thus, as used herein, theaccess controls on the repository may be varied and will depend on thenature of the access limitation. Access controls may be role-based,user-based, or otherwise.

In one particular embodiment, the PSI tool on each side (i.e., on thecloud, and at the entity) implements a cryptographic protocol known asGarbled circuit. Garbled circuit provides a way to compile a programinto a pair of programs each comprising a large number of logical gates.The two programs are configured to connect to one another, e.g., over anetwork, and collaboratively compute to generate an output (answer).Typically, the gates that compose the program are formed using acryptographic primitive, such as oblivious transfer, which can be builtusing asymmetric cryptography, e.g., the Rivest Shamir Adelman (RSA)cryptosystem. In cryptography, an oblivious transfer (OT) protocol is atype of protocol in which a sender transfers one of potentially manypieces of information to a receiver, but the sender remains oblivious asto what piece (if any) has been transferred. In a representativeimplementation, each Garbled circuit program executes in a memory of acomputing system, namely, in a first computing system in the cloud (FIG.5, 500), and in a second computing system associated with the privateentity (FIG. 5, 508). Using this approach, the end result is an overallprogram that executes in two halves simultaneously, and in such a waythat no amount of looking into the contents of memory on eithercomputing system allows determination of which logic path or data valuethe program is using at any moment, up until the program outputs a finalanswer to one or both parties.

The technique of this disclosure provides significant advantages. As hasbeen described, the approach herein provides for a way to detect whetherspecific sensitive data of a client is present in a cloud computinginfrastructure without requiring that data be shared with the cloudprovider, or that the cloud provider provide the client access to all(or even any) data in the cloud. The approach enables sensitive datadetection that does not require DLP or other complex systems to besupported in the enterprise, nor the training of a statisticalclassifier. The PSI-based approach is highly-secure,computationally-efficient, and ensures that sensitive data detection isfacilitated with respect to those entities that have authorized rightsto access the client database for the data detection. To this end, andas has been described each side of the communication preferably executesa PSI agent (tool), which is readily implemented in software.

As used herein, a PSI agent typically is implemented in software, e.g.,as a set of computer program instructions executed by one or morehardware processors. A particular tool may comprise any number ofprograms, processes, execution threads, and the like, together withappropriate interfaces and databases to support data used or created bythe tool. The tool may be configured or administered with a web-basedfront-end, via a command line, or the like. The tool may include one ormore functions that are implemented programmatically, or thatinteroperate with other computing entities or software systems via anapplication programming interface (API), or any convenientrequest-response protocol

As noted above, preferably the approach herein is implemented inassociation with various advanced services, such as a cognitive service.A representative cognitive service is IBM Watson, as has been described.Generalizing, a cognitive service of this type provides for processingof unstructured data sources, typically using a question and answer(Q&A) system, such as a natural language processing (NLP)-basedartificial intelligence (AI) learning machine. A machine of this typemay combine natural language processing, machine learning, andhypothesis generation and evaluation; it receives queries and providesdirect, confidence-based responses to those queries. A Q&A solution suchas IBM Watson may be cloud-based, with the Q&A function delivered“as-a-service” (SaaS) that receives NLP-based queries and returnsappropriate answers.

A representative Q&A system, such as described in U.S. Pat. No.8,275,803, provides answers to questions based on any corpus of data.The method described there facilitates generating a number of candidatepassages from the corpus that answer an input query, and finds thecorrect resulting answer by collecting supporting evidence from themultiple passages. By analyzing all retrieved passages and thatpassage's metadata in parallel, there is generated an output pluralityof data structures including candidate answers based upon the analyzingstep. Then, by each of a plurality of parallel operating modules,supporting passage retrieval operations are performed upon the set ofcandidate answers; for each candidate answer, the data corpus istraversed to find those passages having candidate answer in addition toquery terms. All candidate answers are automatically scored causing thesupporting passages by a plurality of scoring modules, each producing amodule score. The modules scores are processed to determine one or morequery answers; and, a query response is generated for delivery to a userbased on the one or more query answers.

In an alternative embodiment, the Q&A system may be implemented usingIBM LanguageWare, a natural language processing technology that allowsapplications to process natural language text. LanguageWare comprises aset of Java libraries that provide various NLP functions such aslanguage identification, text segmentation and tokenization,normalization, entity and relationship extraction, and semanticanalysis.

The described approach is preferably web- or cloud-based, therebyavoiding traditional installation and deployment issues that oftenaccompany DLP systems. The techniques provide for lightweight tooling(the client-server based PSI tool) to interact with the corpus(cloud-based) and the database (client-based) to detect potentialsensitive data leakage. The approach thus promotes simple and effectivecross-organization collaboration with sufficient privacy to alleviate orameliorate security concerns.

As noted, the references herein to one or more commercial products orservices are exemplary and should not be taken to limit the disclosedtechnique, which may be implemented on any system, device, appliance(or, more generally, machine) having the general characteristics andoperating functionality that has been described.

This subject matter may be implemented as-a-service. As previouslynoted, and without limitation, the subject matter may be implementedwithin or in association with a cloud deployment platform system orappliance, or using any other type of deployment systems, products,devices, programs or processes. As has been described, the PSI tool andrelated response system functionality may be provided as a standalonefunction, or it may leverage functionality from other products andservices.

A representative cloud application platform with which the technique maybe implemented includes, without limitation, any cloud-supportedapplication framework, product or service.

Generalizing, the techniques herein may be implemented as a managementsolution, service, product, appliance, device, process, program,execution thread, or the like. Typically, the techniques are implementedin software, as one or more computer programs executed in hardwareprocessing elements, in association with data stored in one or more datasources, such as a problems database. Some or all of the processingsteps described may be automated and operate autonomously in associationwith other systems. The automation may be full- or partial, and theoperations (in whole or in part) may be synchronous or asynchronous,demand-based, or otherwise.

These above-described components typically are each implemented assoftware, i.e., as a set of computer program instructions executed inone or more hardware processors. The components are shown as distinct,but this is not a requirement, as the components may also be integratedwith one another in whole or in part. One or more of the components mayexecute in a dedicated location, or remote from one another. One or moreof the components may have sub-components that execute together toprovide the functionality. There is no requirement that particularfunctions of the generator service be executed by a particular componentas named above, as the functionality herein (or any aspect thereof) maybe implemented in other or systems.

The tool and response functionality can interact or interoperate withsecurity analytics systems or services.

As has been described, the functionality described above may beimplemented as a standalone approach, e.g., one or more software-basedfunctions executed by one or more hardware processors, or it may beavailable as a managed service (including as a web service via aSOAP/XML interface). The particular hardware and software implementationdetails described herein are merely for illustrative purposes are notmeant to limit the scope of the described subject matter.

More generally, computing devices within the context of the disclosedsubject matter are each a data processing system (such as shown in FIG.2) comprising hardware and software, and these entities communicate withone another over a network, such as the Internet, an intranet, anextranet, a private network, or any other communications medium or link.The applications on the data processing system provide native supportfor Web and other known services and protocols including, withoutlimitation, support for HTTP, FTP, SMTP, SOAP, XML, WSDL, UDDI, andWSFL, among others. Information regarding SOAP, WSDL, UDDI and WSFL isavailable from the World Wide Web Consortium (W3C), which is responsiblefor developing and maintaining these standards; further informationregarding HTTP, FTP, SMTP and XML is available from Internet EngineeringTask Force (IETF).

As noted, and in addition to the cloud-based environment, the techniquesdescribed herein may be implemented in or in conjunction with variousserver-side architectures including simple n-tier architectures, webportals, federated systems, and the like.

Still more generally, the subject matter described herein can take theform of an entirely hardware embodiment, an entirely software embodimentor an embodiment containing both hardware and software elements. In apreferred embodiment, the sensitive data detection service (or anycomponent thereof) is implemented in software, which includes but is notlimited to firmware, resident software, microcode, and the like.Furthermore, the download and delete interfaces and functionality cantake the form of a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. For the purposes of this description, a computer-usable orcomputer readable medium can be any apparatus that can contain or storethe program for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be an electronic, magnetic,optical, electromagnetic, infrared, or a semiconductor system (orapparatus or device). Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD. The computer-readable medium is atangible, non-transitory item.

The computer program product may be a product having programinstructions (or program code) to implement one or more of the describedfunctions. Those instructions or code may be stored in a computerreadable storage medium in a data processing system after beingdownloaded over a network from a remote data processing system. Or,those instructions or code may be stored in a computer readable storagemedium in a server data processing system and adapted to be downloadedover a network to a remote data processing system for use in a computerreadable storage medium within the remote system.

In a representative embodiment, the techniques are implemented in aspecial purpose computing platform, preferably in software executed byone or more processors. The software is maintained in one or more datastores or memories associated with the one or more processors, and thesoftware may be implemented as one or more computer programs.Collectively, this special-purpose hardware and software comprises thefunctionality described above.

While the above describes a particular order of operations performed bycertain embodiments of the invention, it should be understood that suchorder is exemplary, as alternative embodiments may perform theoperations in a different order, combine certain operations, overlapcertain operations, or the like. References in the specification to agiven embodiment indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic.

Finally, while given components of the system have been describedseparately, one of ordinary skill will appreciate that some of thefunctions may be combined or shared in given instructions, programsequences, code portions, and the like.

The Garbled circuit protocol (e.g., using oblivious transfer) asdescribed herein is not intended to be limiting. Any cryptographicprotocol that enables two-party secure computation in which twopotentially mistrusting parties can jointly evaluate a function overtheir private inputs without the presence of a trusted third party maybe used.

Further, Private Set Intersection is just a representative cryptographicprotocol. As an alternative, a Private Search protocol may be used. Inthis embodiment, the corpus is indexed on the cloud and a check isperformed to determine if one or more terms of interest to a requestingclient are in the index.

The techniques herein provide for improvements to another technology ortechnical field, namely, data detection security analysis tools andsystems, and cloud-based systems, as well as improvements to thefunctioning of automated sensitive data detection tools and methods.

Having described our invention, what is claimed is as follows.

1. A method to protect data in association with a cloud computingenvironment, the cloud computing environment comprising a data store inwhich a corpus of information is received and stored, comprising:identifying a first set of data associated with the corpus ofinformation and private to the cloud computing environment, the firstset of data being an index of the corpus of information; upon receipt ofa request from an entity, the entity having a second set of data, thesecond set of data being sensitive data of interest to the entity andprivate to the entity, executing a cryptographic protocol with theentity to detect presence in the first set of data of any of the secondset of data, wherein the cryptographic protocol is executed byevaluating a given cryptographic function over the first and second setsof private data without enabling the entity access to the corpus ofinformation or the first set of private data; and upon detecting in thefirst set of data any of the second set of data, taking a given action.2. The method as described in claim 1 wherein the cryptographic protocolis a Private Set Intersection (PSI) executed collaboratively inassociation with the entity.
 3. The method as described in claim 2wherein the given cryptographic function is a Garbled circuitimplemented using an oblivious transfer cryptographic primitive.
 4. Themethod as described in claim 1 wherein the entity is a third partygovernment or private entity.
 5. The method as described in claim 1further including receiving the corpus of information from the entityvia an application programming interface (API).
 6. The method asdescribed in claim 5 wherein the cryptographic protocol is executedduring ingest of the corpus of information.
 7. The method as describedin claim 1 wherein the given action is one of: providing an alert,labeling any of the second set of data that has been determined to bepresent in the first set of data, applying a token to any of the secondset of data that has been determined to be present in the first set ofdata, and redacting any of the second set of data that has beendetermined to be present in the first set of data.
 8. An apparatus toprotect data in association with a cloud computing environment, thecloud computing environment comprising a data store in which a corpus ofinformation is received and stored, comprising: a processor; computermemory holding computer program instructions executed by the processor,the computer program instructions configured to: identify a first set ofdata associated with the corpus of information and private to the cloudcomputing environment, the first set of data being an index of thecorpus of information; upon receipt of a request from an entity, theentity having a second set of data, the second set of data beingsensitive data of interest to the entity and private to the entity,execute a cryptographic protocol with the entity to detect presence inthe first set of data of any of the second set of data, wherein thecryptographic protocol is executed by evaluating a given cryptographicfunction over the first and second sets of private data without enablingthe entity access to the corpus of information or the first set ofprivate data; and upon detecting in the first set of data any of thesecond set of data, take a given action.
 9. The apparatus as describedin claim 8 wherein the cryptographic protocol is a Private SetIntersection (PSI) executed collaboratively in association with theentity.
 10. The apparatus as described in claim 9 wherein the givencryptographic function is a Garbled circuit and the computer programinstructions are configured to implement an oblivious transfercryptographic primitive.
 11. The apparatus as described in claim 8wherein the entity is a third party government or private entity. 12.The apparatus as described in claim 8 wherein the computer programinstructions are further configured to receive the corpus of informationfrom the entity via an application programming interface (API).
 13. Theapparatus as described in claim 12 wherein the cryptographic protocol isexecuted during ingest of the corpus of information.
 14. The apparatusas described in claim 8 wherein the computer program instructionsconfigured to take the given action include computer programinstructions that provide an action that is one of: provide an alert,label any of the second set of data that has been determined to bepresent in the first set of data, apply a token to any of the second setof data that has been determined to be present in the first set of data,and redact any of the second set of data that has been determined to bepresent in the first set of data.
 15. A computer program product in anon-transitory computer readable medium for use in a data processingsystem to protect data in association with a cloud computingenvironment, the cloud computing environment comprising a data store inwhich a corpus of information is received and stored, the computerprogram product holding computer program instructions that, whenexecuted by the data processing system, are configured to: identify afirst set of data associated with the corpus of information and privateto the cloud computing environment, the first set of data being an indexof the corpus of information; upon receipt at the cloud computingenvironment of a request from an entity, the entity having a second setof data, the second set of data being sensitive data of interest to theentity and private to the entity, execute a cryptographic protocol withthe entity to detect presence in the first set of data of any of thesecond set of data, wherein the cryptographic protocol is executed byevaluating a given cryptographic function over the first and second setsof private data without enabling the entity access to the corpus ofinformation or the first set of private data; and upon detecting in thefirst set of day any of the second set of data, take a given action. 16.The computer program product as described in claim 15 wherein thecryptographic protocol is a Private Set Intersection (PSI) executedcollaboratively in association with the entity.
 17. The computer programproduct as described in claim 16 wherein the given cryptographicfunction is a Garbled circuit and the computer program instructions areconfigured to implement an oblivious transfer cryptographic primitive.18. The computer program product as described in claim 15 wherein theentity is a third party government or private entity.
 19. The computerprogram product as described in claim 15 wherein the computer programinstructions are further configured to receive the corpus of informationfrom the entity via an application programming interface (API).
 20. Thecomputer program product as described in claim 19 wherein thecryptographic protocol is executed during ingest of the corpus ofinformation.
 21. The computer program product as described in claim 15wherein the computer program instructions configured to take the givenaction include computer program instructions that provide one of: analert, label any of the second set of data that has been determined tobe present in the first set of data, apply a token to any of the secondset of data that has been determined to be present in the first set ofdata, and redact any of the second set of data that has been determinedto be present in the first set of data.